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Title:Can Internet search queries help to predict stock market volatility? PDF Logo
Authors:Dimpfl, Thomas
Jank, Stephan
Issue Date:2011
Series/Report no.:University of Tübingen working papers in economics and finance 18
Abstract:This paper studies the dynamics of stock market volatility and retail investor attention measured by internet search queries. We find a strong co-movement of stock market indices' realized volatility and the search queries for their names. Furthermore, Granger causality is bi-directional: high searches follow high volatility, and high volatility follows high searches. Using the latter feedback effect to predict volatility we find that search queries contain additional information about market volatility. They help to improve volatility forecasts in-sample and out-of-sample as well as for different forecasting horizons. Search queries are particularly useful to predict volatility in high-volatility phases.
Subjects:realized volatility
forecasting
investor behavior
noise trader
search engine data
JEL:G10
G14
G17
Persistent Identifier of the first edition:urn:nbn:de:bsz:21-opus-58552
Document Type:Working Paper
Appears in Collections:University of Tübingen Working Papers in Economics and Finance

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